W-Net: Two-Stage U-Net With Misaligned Data for Raw-to-RGB Mapping

@article{Uhm2019WNetTU,
  title={W-Net: Two-Stage U-Net With Misaligned Data for Raw-to-RGB Mapping},
  author={Kwang-Hyun Uhm and Seung-Wook Kim and Seo-Won Ji and Sung-Jin Cho and Jun Pyo Hong and S. Ko},
  journal={2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
  year={2019},
  pages={3636-3642}
}
  • Kwang-Hyun Uhm, Seung-Wook Kim, +3 authors S. Ko
  • Published 2019
  • Engineering, Computer Science
  • 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Recent research on a learning mapping between raw Bayer images and RGB images has progressed with the development of deep convolutional neural network. A challenging data set namely the Zurich Raw-to-RGB data set (ZRR) has been released in the AIM 2019 raw-to-RGB mapping challenge. In ZRR, input raw and target RGB images are captured by two different cameras and thus not perfectly aligned. Moreover, camera metadata such as white balance gains and color correction matrix are not provided, which… Expand
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References

SHOWING 1-10 OF 23 REFERENCES
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
TLDR
An end-to-end deep learning approach that bridges the gap by translating ordinary photos into DSLR-quality images by learning the translation function using a residual convolutional neural network that improves both color rendition and image sharpness. Expand
MSR-net: Low-light Image Enhancement Using Deep Convolutional Network
TLDR
A Convolutional Neural Network (MSR-net) that directly learns an end-to-end mapping between dark and bright images and shows that multi-scale Retinex is equivalent to a feedforward convolutional neural network with different Gaussian convolution kernels. Expand
DeepISP: Toward Learning an End-to-End Image Processing Pipeline
TLDR
The proposed solution achieves the state-of-the-art performance in objective evaluation of peak signal-to-noise ratio on the subtask of joint denoising and demosaicing and achieves better visual quality compared to the manufacturer ISP. Expand
AIM 2019 Challenge on RAW to RGB Mapping: Methods and Results
TLDR
This paper reviews the first AIM challenge on mapping camera RAW toRGB images with the focus on proposed solutions and results, defining the state-of-the-art for RAW to RGB image restoration. Expand
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
TLDR
This work considers image transformation problems, and proposes the use of perceptual loss functions for training feed-forward networks for image transformation tasks, and shows results on image style transfer, where aFeed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time. Expand
Learning to See in the Dark
TLDR
A pipeline for processing low-light images is developed, based on end-to-end training of a fully-convolutional network that operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data. Expand
Deep joint demosaicking and denoising
TLDR
A new data-driven approach forDemosaicking and denoising is introduced: a deep neural network is trained on a large corpus of images instead of using hand-tuned filters and this network and training procedure outperform state-of-the-art both on noisy and noise-free data. Expand
Raw-to-Raw: Mapping between Image Sensor Color Responses
TLDR
An illumination-independent mapping approach that uses white-balancing to assist in reducing the number of required transformations is introduced that achieves state-of-the-art results on a range of consumer cameras and images of arbitrary scenes and illuminations. Expand
Toward Convolutional Blind Denoising of Real Photographs
TLDR
A convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs and a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet. Expand
Very Deep Convolutional Networks for Large-Scale Image Recognition
TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. Expand
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